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Running naive bayes classifier analysis...
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Analyze another fileFast probabilistic classifier that assumes features are independent. Often surprisingly accurate for text classification and quick baselines despite the naive assumption.
Use this as a quick baseline classifier or when features are roughly independent.
If features are highly correlated, use Logistic Regression or XGBoost for better results.
Built for: Data scientist, analyst, student
Typical data source: Labeled dataset with binary or multi-class target
Classification dataset
Minimum 30 rows · Best with 200-10000 rows
Fits a Naive Bayes classifier for binary outcomes. Produces ROC curve with AUC, confusion matrix heatmap, predicted probability distributions by class, feature conditional profiles, and a detailed performance metrics table. Supports both numeric (Gaussian) and categorical predictors with Laplace smoothing.
Receiver operating characteristic with AUC
Classification accuracy breakdown (TP, FP, TN, FN)
Distribution of predicted probabilities by actual class
Feature distributions stratified by outcome class
Accuracy, precision, recall, F1, and AUC
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Chi Square Test — Just testing independence, not predicting
Need more power? Logistic — Need interpretable coefficients
Similar: Xgboost
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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